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BenchLM
Model A
Claude Opus 4.7

Anthropic

71.9/100

Supported · Public rank #15

90% interval 60.1–83.7

Claude Opus 4.7 vs Claude Sonnet 5

Updated August 14, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
Claude Sonnet 5

Anthropic

64.8/100

Estimated · Public rank #37

90% interval 50.5–79.1

Decision reading

Claude Opus 4.7 has the higher public score estimate, 71.87 versus 64.78, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

1 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

  • Chat turn cost

    1K fresh input + 500 output tokens

    Claude Sonnet 5

    Claude Sonnet 5 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

    200K cached + 20K fresh input + 10K output tokens

    Claude Sonnet 5

    Claude Sonnet 5 has the lower estimated token cost for this stated workload. Claude Opus 4.7 has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

Show secondary and unsupported calls

  • Repository review cost

    50K fresh input + 3K output tokens

    Claude Sonnet 5

    Claude Sonnet 5 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

Shared results
1
Claude Opus 4.7 only
7
Claude Sonnet 5 only
18
Like-for-like categories
0 / 8

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Agentic

Not comparable
Claude Opus 4.7
Not measured
Claude Sonnet 5
81.9
Weighted basis
0 vs 3 rows
Reading
Not comparable

Coding

Not comparable
Claude Opus 4.7
Not measured
Claude Sonnet 5
76.7
Weighted basis
0 vs 2 rows
Reading
Not comparable

Reasoning

Not comparable
Claude Opus 4.7
Not measured
Claude Sonnet 5
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Claude Opus 4.7
Not measured
Claude Sonnet 5
57.4
Weighted basis
0 vs 1 rows
Reading
Not comparable

Math

Not comparable
Claude Opus 4.7
38.6
Claude Sonnet 5
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Claude Opus 4.7
Not measured
Claude Sonnet 5
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Claude Opus 4.7
Not measured
Claude Sonnet 5
88.3
Weighted basis
0 vs 1 rows
Reading
Not comparable

Instruction following

Not comparable
Claude Opus 4.7
Not measured
Claude Sonnet 5
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

Claude Opus 4.7
$0.0175
Fits in one request
Claude Sonnet 5
$0.007
Fits in one request

Claude Sonnet 5 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Claude Opus 4.7
$0.325
Fits in one request
Claude Sonnet 5
$0.13
Fits in one request

Claude Sonnet 5 has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

Claude Opus 4.7
$1.35
Fits in one request
Cached input priced at the published list-input rate
Claude Sonnet 5
$0.18
Fits in one request

Claude Sonnet 5 has the lower modeled cost

Claude Opus 4.7 has no published cached-input rate, so cached tokens use its listed input rate.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

Claude Opus 4.7

Not published

Claude Sonnet 5

$0.2 per 1M cached input tokens

Claude API pricing

Reasoning profile

Claude Opus 4.7

Non-Reasoning

Claude Sonnet 5

Reasoning

Weight access

Claude Opus 4.7

Proprietary

Claude Sonnet 5

Proprietary

License

Claude Opus 4.7

Proprietary

Claude Sonnet 5

Proprietary

Release date

Claude Opus 4.7

2026-04-16

Claude Sonnet 5

2026-06-30

If you already use one of these models
Deployment change
Both entries list Anthropic as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
Quality signal
Claude Opus 4.7 has the higher public score estimate, 71.87 versus 64.78, but the 90% score intervals overlap.
Workload cost
Repository review: $0.325 vs $0.13. Cache-heavy agent loop: $1.35 vs $0.18.
Context tradeoff
Both models list 1M.

Run the same representative tasks against both endpoints before changing production traffic.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence26 rows

Agentic

  • Gert Labs

    Claude Opus 4.765.59%
    Source
    Claude Sonnet 5—

    Not directly comparable

  • ResearchClawBench

    Claude Opus 4.720.7%
    Source
    Claude Sonnet 5—

    Not directly comparable

  • OSWorld 2.0

    Claude Opus 4.713.9%
    Source
    Claude Sonnet 5—

    Not directly comparable

  • Terminal-Bench 3.0

    Claude Opus 4.7—
    Claude Sonnet 514.6%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    Claude Opus 4.7—
    Claude Sonnet 580.4%
    Source

    Not directly comparable

  • BrowseComp

    Claude Opus 4.7—
    Claude Sonnet 584.7%
    Source

    Not directly comparable

  • HLE w/ tools

    Claude Opus 4.7—
    Claude Sonnet 557.4%
    Source

    Not directly comparable

  • OSWorld-Verified

    Claude Opus 4.7—
    Claude Sonnet 581.2%
    Source

    Not directly comparable

Coding

  • Vibe Code Bench

    Claude Opus 4.771.00%
    Source
    Claude Sonnet 5—

    Not directly comparable

  • React Native Evals

    Claude Opus 4.782.8%
    Source
    Claude Sonnet 5—

    Not directly comparable

  • FrontierCode 1.1 Main

    Shared source
    Claude Opus 4.738.5%
    Claude Sonnet 542.7%

    Claude Sonnet 5 leads this result

  • SWE-bench Verified

    Claude Opus 4.7—
    Claude Sonnet 585.2%
    Source

    Not directly comparable

  • SWE-bench Pro

    Claude Opus 4.7—
    Claude Sonnet 563.2%
    Source

    Not directly comparable

  • SWE Multilingual

    Claude Opus 4.7—
    Claude Sonnet 578.3%
    Source

    Not directly comparable

  • SWE Multimodal

    Claude Opus 4.7—
    Claude Sonnet 528.1%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    Claude Opus 4.7—
    Claude Sonnet 580.4%
    Source

    Not directly comparable

  • cursorBench32

    Claude Opus 4.7—
    Claude Sonnet 561.5%
    Source

    Not directly comparable

  • APEX-SWE

    Claude Opus 4.7—
    Claude Sonnet 546.4%
    Source

    Not directly comparable

  • EEBench

    Claude Opus 4.7—
    Claude Sonnet 540.3%
    Source

    Not directly comparable

  • 3DCodeBench

    Claude Opus 4.7—
    Claude Sonnet 539.2%
    Source

    Not directly comparable

Knowledge

  • HLE

    Claude Opus 4.7—
    Claude Sonnet 557.4%
    Source

    Not directly comparable

  • HLE w/o tools

    Claude Opus 4.7—
    Claude Sonnet 543.2%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Claude Opus 4.743.793%
    Source
    Claude Sonnet 5—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Claude Opus 4.722.917%
    Source
    Claude Sonnet 5—

    Not directly comparable

Multimodal

  • CharXiv

    Claude Opus 4.7—
    Claude Sonnet 588.3%
    Source

    Not directly comparable

  • CharXiv w/o tools

    Claude Opus 4.7—
    Claude Sonnet 577%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Claude Opus 4.7 or Claude Sonnet 5?

Claude Opus 4.7 has the higher public score estimate, 71.87 versus 64.78, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Claude Opus 4.7 or Claude Sonnet 5?

The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.

Which is better for agentic tasks, Claude Opus 4.7 or Claude Sonnet 5?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, Claude Opus 4.7 or Claude Sonnet 5?

For the stated presets, chat costs $0.0175 on Claude Opus 4.7 and $0.007 on Claude Sonnet 5; repository review costs $0.325 and $0.13; the cache-heavy agent loop costs $1.35 and $0.18. Claude Opus 4.7 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Claude Opus 4.7 or Claude Sonnet 5?

Both models list the same context window, 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 14, 2026

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